A Fully Connectionist Model Generator for Covered First-Order Logic Programs

Authors

Sebastian Bader
Pascal HitzlerFollow
Steffen Holldobler
Andreas Witzel

Document Type

Conference Proceeding

Publication Date

1-1-2007

Abstract

We present a fully connectionist system for the learning of first-order logic programs and the generation of corresponding models: Given a program and a set of training examples, we embed the associated semantic operator into a feed-forward network and train the network using the examples. This results in the learning of first-order knowledge while damaged or noisy data is handled gracefully.

Comments

Presented at the Twentieth International Joint Conference on Artificial Intelligence, Hyderabad, India, January 6-12, 2007.

Repository Citation

Bader, S., Hitzler, P., Holldobler, S., & Witzel, A. (2007). A Fully Connectionist Model Generator for Covered First-Order Logic Programs. Proceedings of the 20th International Joint Conference on Artificial Intelligence, 666-671.
https://corescholar.libraries.wright.edu/cse/87